Geospatial intelligence analysis via semantic lensing
Nikhil Kalghatgi, Aaron Burgman, Erika Darling, Chris Newbern, Kristine Recktenwald, Shawn Chin, Howard Kong · 2006
Geospatial displays typically contain many data layers ranging in type and level of detail that often result in dense, occluded, and cluttered map displays. We investigated a localized, "detail on-demand" filtering strategy called semantic lensing that in certain situations provides a more efficient and desirable approach than global filtering for mitigating clutter and occlusion.An initial formal user study with these semantic lenses has shown their significant value, expediency, and desirability in aiding decision making during real-world tasks. Completion times of geospatial analyses are significantly faster when using lenses and workloads are significantly lower. The research suggests that using lenses may also improve analysts' accuracy when completing complex time-critical geospatial intelligence analyses. Continued work will evaluate additional features and task-specific applicability. Successful evaluation will propose the distribution of such a lens tool to geospatial intelligence analysts.